Muhammad Amith
Impact in
- Health Informatics top 10%
- Health top 10%
- Vaccine Coverage and Hesitancy
Papers in
-
- Semantic Web and Ontologies 10
- Topic Modeling 7
- Natural Language Processing Techniques 6
-
- Biomedical Text Mining and Ontologies 16
- Co-authors
- Cui Tao (40 shared papers)Julie A. Boom (8 shared papers)Kayo Fujimoto (7 shared papers)Zhe He (2 shared papers)Rachel Cunningham (8 shared papers)Juan Antonio Lossio-Ventura (1 shared paper)Jiang Bian (1 shared paper)Kirk Roberts (8 shared papers)
- Journals
- BMC Medical Informatics and Decision Making (7 papers)Journal of Biomedical Semantics (3 papers)BMC Bioinformatics (2 papers)Journal of Medical Internet Research (2 papers)Journal of Biomedical Informatics (2 papers)
- Partner nations
- United StatesAustraliaChina
In The Last Decade
Muhammad Amith
46 papers receiving 450 citations
Peers
Comparison fields: 5 of 100
- Health Informatics 16
- Health 79
- Artificial Intelligence 193
- Health Information Management 23
- Applied Psychology 21
Countries citing papers authored by Muhammad Amith
This map shows the geographic impact of Muhammad Amith's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Muhammad Amith with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Muhammad Amith more than expected).
Fields of papers citing papers by Muhammad Amith
This network shows the impact of papers produced by Muhammad Amith. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Muhammad Amith. The network helps show where Muhammad Amith may publish in the future.
Co-authors
The 25 scholars most cited alongside Muhammad Amith, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 48 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2018 | 71 | |
| 2 | 2020 | 53 | |
| 3 | 2019 | 28 | |
| 4 | 2021 | 20 | |
| 5 | 2019 | 18 | |
| 6 | 2022 | 16 | |
| 7 | 2018 | 15 | |
| 8 | 2018 | 14 | |
| 9 | 2019 | 13 | |
| 10 | 2018 | 12 | |
| 11 | 2019 | 12 | |
| 12 | 2024 | 10 | |
| 13 | 2017 | 10 | |
| 14 | 2015 | 10 | |
| 15 | 2023 | 10 | |
| 16 | 2017 | 10 | |
| 17 | 2017 | 9 | |
| 18 | 2020 | 9 | |
| 19 | 2020 | 8 | |
| 20 | 2020 | 8 |
About Muhammad Amith
Muhammad Amith is a scholar working on Artificial Intelligence, Molecular Biology, Sociology and Political Science, Health and General Health Professions, having authored 48 papers that have together received 461 indexed citations. Recurring topics across this work include Biomedical Text Mining and Ontologies (16 papers), Semantic Web and Ontologies (10 papers), Vaccine Coverage and Hesitancy (9 papers), Misinformation and Its Impacts (7 papers), Topic Modeling (7 papers), Natural Language Processing Techniques (6 papers), HIV/AIDS Research and Interventions (5 papers) and HIV, Drug Use, Sexual Risk (4 papers). The work is most often cited by research in Health Informatics (16 citations), Health (79 citations), Artificial Intelligence (193 citations), Health Information Management (23 citations) and Applied Psychology (21 citations). Muhammad Amith has collaborated with scholars based in United States, Australia and China. Frequent co-authors include Cui Tao, Julie A. Boom, Kayo Fujimoto, Zhe He, Rachel Cunningham, Juan Antonio Lossio-Ventura, Jiang Bian, Kirk Roberts, Lu Tang and Lara S. Savas. Their work appears in journals such as BMC Medical Informatics and Decision Making, Journal of Biomedical Semantics, BMC Bioinformatics, Journal of Medical Internet Research and Journal of Biomedical Informatics.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.